Ferroelectric tunneling junctions (FTJs) with tunable tunneling electroresistance (TER) are promising for many emerging applications, including non-volatile memories and neurosynaptic computing. One of the key challenges in FTJs is the balance between the polarization value and the tunneling current. In order to achieve a sizable on-current, the thickness of the ferroelectric layer needs to be scaled down below 5 nm. However, the polarization in these ultra-thin ferroelectric layers is very small, which leads to a low tunneling electroresistance (TER) ratio. In this paper, we propose and demonstrate a new type of FTJ based on metal/Al2O3/Zr-doped HfO2/Si structure. The interfacial Al2O3 layer and silicon substrate enable sizable TERs even when the thickness of Zr-doped HfO2 (HZO) is above 10 nm. We found that F-N tunneling dominates at read voltages and that the polarization switching in HZO can alter the effective tunneling barrier height and tune the tunneling resistance. The FTJ synapses based on Al2O3/HZO stacks show symmetric potentiation/depression characteristics and widely tunable conductance. We also show that spike-timing-dependent plasticity (STDP) can be harnessed from HZO based FTJs. These novel FTJs will have high potential in non-volatile memories and neural network applications.

How impurity atoms move through a crystal is a fundamental and recurrent question in materials. The previous models of oxygen diffusion in titanium relied on interstitial lattice sites that were recently found to be unstable--leaving no consistent picture of the diffusion pathways. Using first-principles quantum-mechanical methods, we find three oxygen interstitial sites in titanium, and quantify the multiple interpenetrating networks for oxygen diffusion. Surprisingly, all transitions contribute to diffusion.

We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. We apply a correction method for solute diffusion in alloys using experimental and simulated values of host self-diffusivity. We find good agreement with experimental solute diffusion data, obtaining a weighted activation barrier RMS error of 0.176 eV when excluding magnetic solutes in non-magnetic alloys. The compiled database is the largest collection of consistently calculated ab-initio solute diffusion data in the world.

We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural network (ANN). We separately optimize key physical descriptors favored by each method to model diffusion barriers. We also assess the ability of each method to extrapolate when faced with new hosts with limited known data. GKRR and ANN were found to perform the best, showing 0.15 eV cross-validation errors and predicting impurity diffusion in new hosts to within 0.2 eV when given only 5 data points from the host. We demonstrate the success of a combined DFT + data mining approach towards solving materials science challenges and predict the diffusion barrier of all available impurities across all FCC hosts.Keywords: Diffusion; Data-mining; Machine learning; DFT; Neural network 1: Introduction Atomic migration in solids governs the kinetics of many materials processes, including precipitation, high-temperature creep, phase transformation, and solution homogenization. A particular class of atomic diffusion is dilute impurity diffusion, which refers to the diffusion of a dilute solute in a host. Such diffusion is relevant in many materials applications as dilute solutes are common due to either undesired impurities or intentional dopants in materials. Due to its importance to materials science, large experimental catalogues of impurity diffusion measurements have been collected [1,2], and more recently, first-principles predictions of dilute impurity diffusion coefficients have been conducted [3][4][5][6][7][8]. However, both experimental and theoretical approaches are limited by several drawbacks. Experimental diffusivities often vary significantly due to uncertainties introduced by different measurement techniques and other impurity effects in sample materials. In addition, experiments require significant diffusion kinetics to achieve good data, which generally limits them to be relatively high temperature (e.g., above about 50% of the melting temperature of the host) [9]. Finally, experiments are time consuming and expensive relative to first-principles calculations, as they require significant equipment and human interaction. First-principles calculations are an increasingly powerful tool for predicting dilute impurity diffusion, and compared to experiments can be done at a tiny fraction of the cost of equipment and human time. Furthermore, first-principles predicted energies are expected to be most accurate at lower temperatures, where vibrational and electronic excitations play a minor role, which suggests that these methods may have their best accuracy in temperature domains complimentary to experiments. First-principles methods bring with them significant approximations in both the fundamental energetics and in trea...

This work demonstrates how databases of diffusion-related properties can be developed from high-throughput ab initio calculations. The formation and migration energies for vacancies of all adequately stable pure elements in both the face-centered cubic (fcc) and hexagonal close packing (hcp) crystal structures were determined using ab initio calculations. For hcp migration, both the basal plane and z-direction nearest-neighbor vacancy hops were considered. Energy barriers were successfully calculated for 49 elements in the fcc structure and 44 elements in the hcp structure. These data were plotted against various elemental properties in order to discover significant correlations. The calculated data show smooth and continuous trends when plotted against Mendeleev numbers. The vacancy formation energies were plotted against cohesive energies to produce linear trends with regressed slopes of 0.317 and 0.323 for the fcc and hcp structures respectively. This result shows the expected increase in vacancy formation energy with stronger bonding. The slope of approximately 0.3, being well below that predicted by a simple fixed bond strength model, is consistent with a reduction in the vacancy formation energy due to many-body effects and relaxation. Vacancy migration barriers are found to increase nearly linearly New J. Phys. 16 (2014) 015018 T Angsten et al with increasing stiffness, consistent with the local expansion required to migrate an atom. A simple semi-empirical expression is created to predict the vacancy migration energy from the lattice constant and bulk modulus for fcc systems, yielding estimates with errors of approximately 30%.

A binary embedded-atom method (EAM) potential is optimized for Cu on Ag(111) by fitting to ab initio data. The fitting database consists of DFT calculations of Cu monomers and dimers on Ag(111), specifically their relative energies, adatom heights, and dimer separations. We start from the Mishin Cu-Ag EAM potential and first modify the Cu-Ag pair potential to match the FCC/HCP site energy difference then include Cu-Cu pair potential optimization for the entire database. The optimized EAM potential reproduce DFT monomer and dimer relative energies and geometries correctly. In trimer calculations, the potential produces the DFT relative energy between FCC and HCP trimers, though a different ground state is predicted. We use the optimized potential to calculate diffusion barriers for Cu monomers, dimers, and trimers. The predicted monomer barrier is the same as DFT, while experimental barriers for monomers and dimers are both lower than predicted here. We attribute the difference with experiment to the overestimation of surface adsorption energies by DFT and a simple correction is presented. Our results show that the optimized Cu-Ag EAM can be applied in the study of larger Cu islands on Ag(111).

The MAterials Simulation Toolkit (MAST) is a workflow manager and post-processing tool for ab initio defect and diffusion workflows. MAST codifies research knowledge and best--practices for such workflows, and allows for the generation and management of easily modified and reproducible workflows, where data is stored along with workflow information for data provenance tracking. MAST is available for download through the Python Package Index, or at https://pypi.python.org/pypi/MAST, with installation instructions and a detailed user's guide at http://pythonhosted.org/MAST. MAST code may be browsed at the GitHub repository at https://github.com/uw--cmg/MAST.

Abstract:With the best overall electronic and thermal properties, single crystal diamond (SCD) is the extreme wide bandgap material that is expected to revolutionize power electronics and radio-frequency electronics in the future. However, turning SCD into useful semiconductors requires overcoming doping challenges, as conventional substitutional doping techniques, such as thermal diffusion and ion implantation, are not easily applicable to SCD. Here we report a simple and easily accessible doping strategy demonstrating that electrically activated, substitutional doping in SCD without inducing graphitization transition or lattice damage can be readily realized with thermal diffusion at relatively low temperatures by using heavily doped Si nanomembranes as a unique dopant carrying medium. Atomistic simulations elucidate a vacancy exchange boron doping mechanism that occur at the bonded interface between Si and diamond.We further demonstrate selectively doped high voltage diodes and half-wave rectifier circuits using such doped SCD. Our new doping strategy has established a reachable path toward using SCDs for future high voltage power conversion systems and for other novel diamond based electronic devices. The novel doping mechanism may find its critical use in other wide bandgap semiconductors.With the advent of various new renewable energy sources and the emerging need to deliver and convert energy more efficiently, power electronics have received unprecedented attention in recent years. For the last several decades, Si-based power devices have played a dominant role in power conversion electronics. Wide bandgap semiconductor material based power electronics, such as those employing GaN and SiC, are expected to handle more power with higher efficiency than Si-based ones. GaN exhibits higher saturation velocity than Si. However, the thermal conductivity of GaN is low for power conversion systems. Moreover, it is currently difficult to obtain a thick and high quality GaN layer. SiC has its own native substrate, but it has inferior performance matrices (e.g., Johnson's figure of merit) versus GaN. In comparison, diamond exhibits most of the critical material properties for power electronics, except for its small substrate size at present. Diamond has a wide bandgap, high critical electric field, high carrier mobility, high carrier saturation velocities and the highest thermal conductivity among all available semiconductor materials [1][2][3] . Due to its superior electrical properties, the thickness of the highest quality diamond required to block an equivalent amount of voltage is approximately onefifth to one-fourth the thicknesses of GaN or SiC. In particular, the superior thermal conductivity of diamond could greatly simplify the design of heat dissipation and hence simplify entire power electronics modules. Therefore, diamond is considered the best material candidate for power electronics in terms of power switching efficiency, reliability, and system volume and weight.However, besides the lack of large ar...